
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt


life_expectancy_df = pd.read_csv('Life_Expectancy_Data.csv')


life_expectancy_df


sns.heatmap(life_expectancy_df.isnull(), yticklabels = False, cbar = False, cmap="Blues")

<AxesSubplot:>


life_expectancy_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2938 entries, 0 to 2937
Data columns (total 21 columns):
 #   Column                           Non-Null Count  Dtype  
---  ------                           --------------  -----  
 0   Year                             2938 non-null   int64  
 1   Status                           2938 non-null   object 
 2   Life expectancy                  2928 non-null   float64
 3   Adult Mortality                  2928 non-null   float64
 4   infant deaths                    2938 non-null   int64  
 5   Alcohol                          2744 non-null   float64
 6   percentage expenditure           2938 non-null   float64
 7   Hepatitis B                      2385 non-null   float64
 8   Measles                          2938 non-null   int64  
 9    BMI                             2904 non-null   float64
 10  under-five deaths                2938 non-null   int64  
 11  Polio                            2919 non-null   float64
 12  Total expenditure                2712 non-null   float64
 13  Diphtheria                       2919 non-null   float64
 14   HIV/AIDS                        2938 non-null   float64
 15  GDP                              2490 non-null   float64
 16  Population                       2286 non-null   float64
 17   thinness  1-19 years            2904 non-null   float64
 18   thinness 5-9 years              2904 non-null   float64
 19  Income composition of resources  2771 non-null   float64
 20  Schooling                        2775 non-null   float64
dtypes: float64(16), int64(4), object(1)
memory usage: 482.1+ KB


life_expectancy_df.describe()


# Plot the histogram
life_expectancy_df.hist(bins = 30, figsize = (20, 20), color = 'b');


plt.figure(figsize = (20,20))
sns.pairplot(life_expectancy_df)

<seaborn.axisgrid.PairGrid at 0x2b699880220>

<Figure size 1440x1440 with 0 Axes>


sns.scatterplot(data = life_expectancy_df, x = 'Schooling', y = 'Life expectancy ')

<AxesSubplot:xlabel='Schooling', ylabel='Life expectancy '>


sns.scatterplot(data = life_expectancy_df, x = 'GDP', y = 'Life expectancy ')

<AxesSubplot:xlabel='GDP', ylabel='Life expectancy '>


sns.scatterplot(data = life_expectancy_df, x = 'Income composition of resources', y = 'Life expectancy ')

<AxesSubplot:xlabel='Income composition of resources', ylabel='Life expectancy '>


sns.scatterplot(data = life_expectancy_df, x = 'Population', y = 'Schooling')

<AxesSubplot:xlabel='Population', ylabel='Schooling'>


sns.scatterplot(data = life_expectancy_df, x = ' HIV/AIDS', y = 'Life expectancy ')

<AxesSubplot:xlabel=' HIV/AIDS', ylabel='Life expectancy '>


sns.scatterplot(data = life_expectancy_df, x = 'Income composition of resources', y = 'Life expectancy ')

<AxesSubplot:xlabel='Income composition of resources', ylabel='Life expectancy '>


plt.figure(figsize = (20,20))
corr_matrix = life_expectancy_df.corr()
sns.heatmap(corr_matrix, annot = True)
plt.show()


life_expectancy_df


# Checking the unique values in country to consider it as a categorical variable
life_expectancy_df['Status'].nunique()

2


life_expectancy_df = pd.get_dummies(life_expectancy_df, columns = ['Status'])


life_expectancy_df


life_expectancy_df.isnull().sum()[np.where(life_expectancy_df.isnull().sum() != 0)[0]]

Life expectancy                     10
Adult Mortality                     10
Alcohol                            194
Hepatitis B                        553
 BMI                                34
Polio                               19
Total expenditure                  226
Diphtheria                          19
GDP                                448
Population                         652
 thinness  1-19 years               34
 thinness 5-9 years                 34
Income composition of resources    167
Schooling                          163
dtype: int64


life_expectancy_df = life_expectancy_df.apply(lambda x: x.fillna(x.mean()),axis=0)


life_expectancy_df.isnull().sum()[np.where(life_expectancy_df.isnull().sum() != 0)[0]]

Series([], dtype: int64)


X = life_expectancy_df.drop(columns = ['Life expectancy '])
y = life_expectancy_df[['Life expectancy ']]

X

y


X.shape

(2938, 21)


y.shape

(2938, 1)


# Convert the data type to float32

X = np.array(X).astype('float32')
y = np.array(y).astype('float32')


# Only take the numerical variables and scale them
X

array([[2.015e+03, 2.630e+02, 6.200e+01, ..., 1.010e+01, 0.000e+00,
        1.000e+00],
       [2.014e+03, 2.710e+02, 6.400e+01, ..., 1.000e+01, 0.000e+00,
        1.000e+00],
       [2.013e+03, 2.680e+02, 6.600e+01, ..., 9.900e+00, 0.000e+00,
        1.000e+00],
       ...,
       [2.002e+03, 7.300e+01, 2.500e+01, ..., 1.000e+01, 0.000e+00,
        1.000e+00],
       [2.001e+03, 6.860e+02, 2.500e+01, ..., 9.800e+00, 0.000e+00,
        1.000e+00],
       [2.000e+03, 6.650e+02, 2.400e+01, ..., 9.800e+00, 0.000e+00,
        1.000e+00]], dtype=float32)


# split the data into test and train sets
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)


# Scale the data
from sklearn.preprocessing import StandardScaler

scaler_X = StandardScaler()
X_train = scaler_X.fit_transform(X_train)
X_test = scaler_X.transform(X_test)

scaler_y = StandardScaler()
y_train = scaler_y.fit_transform(y_train)
y_test = scaler_y.transform(y_test)


# using linear regression model
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, accuracy_score

regresssion_model_sklearn = LinearRegression(fit_intercept = True)
regresssion_model_sklearn.fit(X_train, y_train)

LinearRegression()


regresssion_model_sklearn_accuracy = regresssion_model_sklearn.score(X_test, y_test)
regresssion_model_sklearn_accuracy

0.8340892521270633


print('Linear Model Coefficient (m): ', regresssion_model_sklearn.coef_)
print('Linear Model Coefficient (b): ', regresssion_model_sklearn.intercept_)

Linear Model Coefficient (m):  [[-2.61061708e-03 -2.48314038e-01  1.30889893e+00  3.24076451e-02
   1.29710045e-02 -2.60179937e-02 -2.49288455e-02  9.39102769e-02
  -1.33250380e+00  6.82824329e-02  2.02552099e-02  9.44694877e-02
  -2.55512834e-01  5.85425347e-02 -1.13813148e-03 -3.10914405e-02
  -1.38065196e-03  1.18575670e-01  2.33237758e-01  2.63141207e-02
  -2.63130777e-02]]
Linear Model Coefficient (b):  [-2.6998622e-08]


# Make prediction

y_predict = regresssion_model_sklearn.predict(X_test)


y_predict

array([[ 5.52710831e-01],
       [-2.16832328e+00],
       [ 1.67019397e-01],
       [-2.60561049e-01],
       [ 6.41681075e-01],
       [ 9.02905345e-01],
       [-1.15051806e+00],
       [ 2.54885584e-01],
       [-6.32353783e-01],
       [-2.10694575e+00],
       [ 8.92400503e-01],
       [-3.87898594e-01],
       [ 4.61621553e-01],
       [ 3.12996089e-01],
       [-2.75252271e+00],
       [ 4.58067030e-01],
       [ 3.05019468e-01],
       [ 1.12625110e+00],
       [ 7.49843001e-01],
       [-1.09040451e+00],
       [ 6.60644352e-01],
       [ 1.71031833e-01],
       [-3.84888314e-02],
       [-8.21287632e-02],
       [ 1.01489449e+00],
       [ 3.93126875e-01],
       [-1.03998697e+00],
       [ 4.70234126e-01],
       [ 6.57431364e-01],
       [ 2.32481062e-01],
       [-1.52942050e+00],
       [-5.84325194e-01],
       [-1.73759401e+00],
       [ 2.12526619e-01],
       [-1.20008457e+00],
       [-5.68039119e-01],
       [-1.86422586e-01],
       [-2.44574212e-02],
       [-3.50263268e-01],
       [ 8.95781815e-02],
       [ 4.71137226e-01],
       [-9.68977809e-01],
       [ 6.29414976e-01],
       [ 1.71904892e-01],
       [ 1.27129161e+00],
       [-1.93901025e-02],
       [-5.66162646e-01],
       [-1.32934423e-02],
       [-1.93406105e+00],
       [-2.66107631e+00],
       [ 9.77280855e-01],
       [-2.06961229e-01],
       [ 6.55054390e-01],
       [ 5.82997799e-01],
       [-1.65501207e-01],
       [ 1.43740594e+00],
       [-2.34375715e+00],
       [-1.17040658e+00],
       [-1.31018746e+00],
       [ 3.55744928e-01],
       [ 6.49780273e-01],
       [ 5.06207407e-01],
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       [ 8.60826612e-01],
       [ 5.91302872e-01],
       [-4.77294289e-02],
       [-2.68146825e+00],
       [ 5.24610162e-01],
       [ 4.77833807e-01],
       [ 6.54073179e-01],
       [ 8.04929674e-01],
       [-1.73394442e+00],
       [ 1.07581782e+00],
       [-8.08958292e-01],
       [ 2.67772347e-01],
       [ 9.14890110e-01],
       [-7.71501303e-01],
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       [-4.25266894e-03],
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       [-9.36321557e-01],
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       [ 3.92089523e-02],
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       [-3.06014329e-01],
       [ 1.22365415e+00],
       [ 5.78518629e-01],
       [-1.00798249e-01],
       [ 7.60962963e-01],
       [ 2.37028420e-01],
       [ 8.12666357e-01],
       [-1.79120839e-01],
       [ 1.27772665e+00],
       [-2.45546317e+00],
       [ 6.60988927e-01],
       [ 3.86145175e-01],
       [-7.20776260e-01],
       [ 7.69019365e-01],
       [ 6.39683366e-01],
       [-9.15374458e-01],
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       [ 1.26638865e+00],
       [ 4.21032578e-01],
       [ 4.30117160e-01],
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       [-8.91006649e-01],
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       [ 2.05942899e-01],
       [-1.02481306e-01],
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       [-9.72267807e-01],
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       [ 5.24665713e-01],
       [-6.11437500e-01],
       [-2.89502740e+00],
       [ 7.83232391e-01],
       [-6.30766630e-01],
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       [ 3.67545694e-01],
       [ 1.12801111e+00],
       [ 8.11241329e-01],
       [ 1.63147950e+00],
       [-1.67399645e+00],
       [ 4.60181266e-01],
       [-2.49360538e+00],
       [-3.21395956e-02],
       [ 1.04973352e+00],
       [-3.01008511e+00],
       [-3.31538647e-01],
       [ 9.03196037e-01],
       [-1.40111303e+00],
       [ 7.18792498e-01],
       [-1.54014075e+00],
       [-1.30988419e-01],
       [ 2.09673703e-01],
       [-8.79785240e-01],
       [-3.52250785e-01],
       [-1.62849927e+00],
       [ 8.13203275e-01],
       [ 4.73319203e-01],
       [ 5.47543526e-01],
       [ 1.43717563e+00],
       [-6.57539010e-01],
       [ 1.80131523e-03],
       [-1.02453232e+00],
       [-6.03744350e-02],
       [-5.91808915e-01],
       [ 6.97400570e-01],
       [-6.51037455e-01],
       [ 6.21403992e-01],
       [ 6.16609871e-01],
       [ 8.89318705e-01],
       [ 4.62804645e-01],
       [ 1.91137537e-01],
       [-4.52241033e-01],
       [-3.87682676e+00],
       [-5.14699399e-01],
       [ 4.73519824e-02],
       [-4.67809401e-02],
       [-9.39416349e-01],
       [ 1.26614523e+00],
       [ 5.33868432e-01],
       [ 8.69098723e-01],
       [ 5.28575540e-01],
       [ 4.38616961e-01],
       [ 7.70562053e-01],
       [-1.46373487e+00],
       [ 4.50699151e-01],
       [ 1.96949631e-01],
       [ 6.12041712e-01],
       [ 7.64083028e-01],
       [-3.52741361e+00],
       [ 1.45277262e-01],
       [ 1.65612787e-01],
       [ 4.38533425e-01],
       [-2.48667645e+00],
       [ 1.92773968e-01],
       [ 4.88228410e-01],
       [ 9.40659583e-01],
       [-1.02997154e-01],
       [ 4.82853390e-02],
       [-1.28369749e+00],
       [ 4.70904440e-01],
       [-9.93004322e-01],
       [ 1.41880476e+00],
       [-3.03581744e-01],
       [ 6.51034653e-01],
       [ 7.43983090e-01],
       [ 1.19432032e+00],
       [ 6.92491651e-01],
       [ 8.07432175e-01],
       [-9.82738376e-01],
       [ 9.22874391e-01],
       [ 6.67386532e-01],
       [ 1.28300339e-01],
       [ 1.52864009e-01],
       [ 4.68493134e-01],
       [-2.14909577e+00],
       [-4.78457987e-01],
       [ 3.02116156e-01],
       [-8.83207917e-01],
       [ 6.83480680e-01],
       [ 2.82940604e-02],
       [-2.89943755e-01],
       [ 7.12881625e-01],
       [ 1.29372728e+00],
       [-1.45629495e-01],
       [ 9.66929495e-02],
       [-2.03770113e+00],
       [ 4.70364362e-01],
       [ 2.04002596e-02],
       [ 1.28312588e+00],
       [ 2.34469458e-01],
       [-1.00727707e-01],
       [ 3.97281684e-02],
       [-1.65420103e+00],
       [ 1.51657537e-01],
       [ 1.04991764e-01],
       [ 6.52542591e-01],
       [-6.94869339e-01],
       [ 7.68318832e-01],
       [-2.69931525e-01],
       [-2.84774117e-02],
       [ 2.28126213e-01],
       [ 9.32809949e-01],
       [-2.49768585e-01],
       [ 1.96041882e-01],
       [ 6.80984080e-01],
       [ 2.54315346e-01],
       [-1.85379517e+00],
       [-8.01015198e-01],
       [ 4.35611635e-01],
       [ 1.02972734e+00],
       [-3.54356766e-01],
       [-1.18666327e+00],
       [ 4.95746523e-01],
       [-9.84485805e-01],
       [ 3.38030964e-01],
       [-1.34528124e+00],
       [ 6.79492831e-01],
       [ 1.31230009e+00],
       [ 2.68432260e-01],
       [ 3.62544395e-02],
       [ 1.18392631e-01],
       [ 7.56380737e-01],
       [ 8.19890374e-06],
       [ 2.67845720e-01],
       [-1.88982773e+00],
       [-1.96749878e+00],
       [ 3.13791670e-02],
       [-6.87573671e-01],
       [-1.71962702e+00],
       [-3.93082786e+00],
       [ 1.52391875e+00],
       [-2.10408717e-01],
       [ 5.26753783e-01],
       [ 1.66754222e+00],
       [-2.53525615e-01],
       [-7.00067699e-01],
       [ 7.59080768e-01],
       [ 3.82731289e-01],
       [ 6.41299069e-01],
       [-2.95760989e+00],
       [ 6.11043513e-01],
       [ 6.03483140e-01],
       [ 3.93115103e-01],
       [ 1.07154775e+00],
       [ 4.79877323e-01],
       [ 1.27206719e+00],
       [ 4.12568241e-01],
       [ 5.83164155e-01],
       [ 1.12685755e-01],
       [-9.65158761e-01],
       [-8.67547333e-01],
       [ 3.75361890e-01],
       [ 4.01545793e-01],
       [ 5.47808230e-01],
       [ 7.25134730e-01],
       [-5.31004846e-01]], dtype=float32)


# Plot the scaled result

plt.plot(y_test, y_predict, "^", color = 'b')
plt.xlabel('Model Predictions')
plt.ylabel('True Values')

Text(0, 0.5, 'True Values')


y_predict_orig = scaler_y.inverse_transform(y_predict)
y_test_orig = scaler_y.inverse_transform(y_test)


# Plot the original values

plt.plot(y_test_orig, y_predict_orig, "^", color = 'b')
plt.xlabel('Model Predictions')
plt.ylabel('True Values')

Text(0, 0.5, 'True Values')


# Plot the KPIs

from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
from math import sqrt

k = X_test.shape[1]
n = len(X_test)
RMSE = float(format(np.sqrt(mean_squared_error(y_test_orig, y_predict_orig)),'.3f'))
MSE = mean_squared_error(y_test_orig, y_predict_orig)
MAE = mean_absolute_error(y_test_orig, y_predict_orig)
r2 = r2_score(y_test_orig, y_predict_orig)
adj_r2 = 1-(1-r2)*(n-1)/(n-k-1)

print('RMSE =',RMSE, '\nMSE =',MSE, '\nMAE =',MAE, '\nR2 =', r2, '\nAdjusted R2 =', adj_r2)

RMSE = 3.993 
MSE = 15.946937 
MAE = 2.9852057 
R2 = 0.8340892506152433 
Adjusted R2 = 0.8279335514331234


!jupyter nbconvert  .ipynb --to html

	Year	Status	Life expectancy	Adult Mortality	infant deaths	Alcohol	percentage expenditure	Hepatitis B	Measles	BMI	...	Polio	Total expenditure	Diphtheria	HIV/AIDS	GDP	Population	thinness 1-19 years	thinness 5-9 years	Income composition of resources	Schooling
0	2015	Developing	65.0	263.0	62	0.01	71.279624	65.0	1154	19.1	...	6.0	8.16	65.0	0.1	584.259210	33736494.0	17.2	17.3	0.479	10.1
1	2014	Developing	59.9	271.0	64	0.01	73.523582	62.0	492	18.6	...	58.0	8.18	62.0	0.1	612.696514	327582.0	17.5	17.5	0.476	10.0
2	2013	Developing	59.9	268.0	66	0.01	73.219243	64.0	430	18.1	...	62.0	8.13	64.0	0.1	631.744976	31731688.0	17.7	17.7	0.470	9.9
3	2012	Developing	59.5	272.0	69	0.01	78.184215	67.0	2787	17.6	...	67.0	8.52	67.0	0.1	669.959000	3696958.0	17.9	18.0	0.463	9.8
4	2011	Developing	59.2	275.0	71	0.01	7.097109	68.0	3013	17.2	...	68.0	7.87	68.0	0.1	63.537231	2978599.0	18.2	18.2	0.454	9.5
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2933	2004	Developing	44.3	723.0	27	4.36	0.000000	68.0	31	27.1	...	67.0	7.13	65.0	33.6	454.366654	12777511.0	9.4	9.4	0.407	9.2
2934	2003	Developing	44.5	715.0	26	4.06	0.000000	7.0	998	26.7	...	7.0	6.52	68.0	36.7	453.351155	12633897.0	9.8	9.9	0.418	9.5
2935	2002	Developing	44.8	73.0	25	4.43	0.000000	73.0	304	26.3	...	73.0	6.53	71.0	39.8	57.348340	125525.0	1.2	1.3	0.427	10.0
2936	2001	Developing	45.3	686.0	25	1.72	0.000000	76.0	529	25.9	...	76.0	6.16	75.0	42.1	548.587312	12366165.0	1.6	1.7	0.427	9.8
2937	2000	Developing	46.0	665.0	24	1.68	0.000000	79.0	1483	25.5	...	78.0	7.10	78.0	43.5	547.358879	12222251.0	11.0	11.2	0.434	9.8

	Year	Life expectancy	Adult Mortality	infant deaths	Alcohol	percentage expenditure	Hepatitis B	Measles	BMI	under-five deaths	Polio	Total expenditure	Diphtheria	HIV/AIDS	GDP	Population	thinness 1-19 years	thinness 5-9 years	Income composition of resources	Schooling
count	2938.000000	2928.000000	2928.000000	2938.000000	2744.000000	2938.000000	2385.000000	2938.000000	2904.000000	2938.000000	2919.000000	2712.00000	2919.000000	2938.000000	2490.000000	2.286000e+03	2904.000000	2904.000000	2771.000000	2775.000000
mean	2007.518720	69.224932	164.796448	30.303948	4.602861	738.251295	80.940461	2419.592240	38.321247	42.035739	82.550188	5.93819	82.324084	1.742103	7483.158469	1.275338e+07	4.839704	4.870317	0.627551	11.992793
std	4.613841	9.523867	124.292079	117.926501	4.052413	1987.914858	25.070016	11467.272489	20.044034	160.445548	23.428046	2.49832	23.716912	5.077785	14270.169342	6.101210e+07	4.420195	4.508882	0.210904	3.358920
min	2000.000000	36.300000	1.000000	0.000000	0.010000	0.000000	1.000000	0.000000	1.000000	0.000000	3.000000	0.37000	2.000000	0.100000	1.681350	3.400000e+01	0.100000	0.100000	0.000000	0.000000
25%	2004.000000	63.100000	74.000000	0.000000	0.877500	4.685343	77.000000	0.000000	19.300000	0.000000	78.000000	4.26000	78.000000	0.100000	463.935626	1.957932e+05	1.600000	1.500000	0.493000	10.100000
50%	2008.000000	72.100000	144.000000	3.000000	3.755000	64.912906	92.000000	17.000000	43.500000	4.000000	93.000000	5.75500	93.000000	0.100000	1766.947595	1.386542e+06	3.300000	3.300000	0.677000	12.300000
75%	2012.000000	75.700000	228.000000	22.000000	7.702500	441.534144	97.000000	360.250000	56.200000	28.000000	97.000000	7.49250	97.000000	0.800000	5910.806335	7.420359e+06	7.200000	7.200000	0.779000	14.300000
max	2015.000000	89.000000	723.000000	1800.000000	17.870000	19479.911610	99.000000	212183.000000	87.300000	2500.000000	99.000000	17.60000	99.000000	50.600000	119172.741800	1.293859e+09	27.700000	28.600000	0.948000	20.700000

	Year	Status	Life expectancy	Adult Mortality	infant deaths	Alcohol	percentage expenditure	Hepatitis B	Measles	BMI	...	Polio	Total expenditure	Diphtheria	HIV/AIDS	GDP	Population	thinness 1-19 years	thinness 5-9 years	Income composition of resources	Schooling
0	2015	Developing	65.0	263.0	62	0.01	71.279624	65.0	1154	19.1	...	6.0	8.16	65.0	0.1	584.259210	33736494.0	17.2	17.3	0.479	10.1
1	2014	Developing	59.9	271.0	64	0.01	73.523582	62.0	492	18.6	...	58.0	8.18	62.0	0.1	612.696514	327582.0	17.5	17.5	0.476	10.0
2	2013	Developing	59.9	268.0	66	0.01	73.219243	64.0	430	18.1	...	62.0	8.13	64.0	0.1	631.744976	31731688.0	17.7	17.7	0.470	9.9
3	2012	Developing	59.5	272.0	69	0.01	78.184215	67.0	2787	17.6	...	67.0	8.52	67.0	0.1	669.959000	3696958.0	17.9	18.0	0.463	9.8
4	2011	Developing	59.2	275.0	71	0.01	7.097109	68.0	3013	17.2	...	68.0	7.87	68.0	0.1	63.537231	2978599.0	18.2	18.2	0.454	9.5
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2933	2004	Developing	44.3	723.0	27	4.36	0.000000	68.0	31	27.1	...	67.0	7.13	65.0	33.6	454.366654	12777511.0	9.4	9.4	0.407	9.2
2934	2003	Developing	44.5	715.0	26	4.06	0.000000	7.0	998	26.7	...	7.0	6.52	68.0	36.7	453.351155	12633897.0	9.8	9.9	0.418	9.5
2935	2002	Developing	44.8	73.0	25	4.43	0.000000	73.0	304	26.3	...	73.0	6.53	71.0	39.8	57.348340	125525.0	1.2	1.3	0.427	10.0
2936	2001	Developing	45.3	686.0	25	1.72	0.000000	76.0	529	25.9	...	76.0	6.16	75.0	42.1	548.587312	12366165.0	1.6	1.7	0.427	9.8
2937	2000	Developing	46.0	665.0	24	1.68	0.000000	79.0	1483	25.5	...	78.0	7.10	78.0	43.5	547.358879	12222251.0	11.0	11.2	0.434	9.8

	Year	Life expectancy	Adult Mortality	infant deaths	Alcohol	percentage expenditure	Hepatitis B	Measles	BMI	under-five deaths	...	Diphtheria	HIV/AIDS	GDP	Population	thinness 1-19 years	thinness 5-9 years	Income composition of resources	Schooling	Status_Developed	Status_Developing
0	2015	65.0	263.0	62	0.01	71.279624	65.0	1154	19.1	83	...	65.0	0.1	584.259210	33736494.0	17.2	17.3	0.479	10.1	0	1
1	2014	59.9	271.0	64	0.01	73.523582	62.0	492	18.6	86	...	62.0	0.1	612.696514	327582.0	17.5	17.5	0.476	10.0	0	1
2	2013	59.9	268.0	66	0.01	73.219243	64.0	430	18.1	89	...	64.0	0.1	631.744976	31731688.0	17.7	17.7	0.470	9.9	0	1
3	2012	59.5	272.0	69	0.01	78.184215	67.0	2787	17.6	93	...	67.0	0.1	669.959000	3696958.0	17.9	18.0	0.463	9.8	0	1
4	2011	59.2	275.0	71	0.01	7.097109	68.0	3013	17.2	97	...	68.0	0.1	63.537231	2978599.0	18.2	18.2	0.454	9.5	0	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2933	2004	44.3	723.0	27	4.36	0.000000	68.0	31	27.1	42	...	65.0	33.6	454.366654	12777511.0	9.4	9.4	0.407	9.2	0	1
2934	2003	44.5	715.0	26	4.06	0.000000	7.0	998	26.7	41	...	68.0	36.7	453.351155	12633897.0	9.8	9.9	0.418	9.5	0	1
2935	2002	44.8	73.0	25	4.43	0.000000	73.0	304	26.3	40	...	71.0	39.8	57.348340	125525.0	1.2	1.3	0.427	10.0	0	1
2936	2001	45.3	686.0	25	1.72	0.000000	76.0	529	25.9	39	...	75.0	42.1	548.587312	12366165.0	1.6	1.7	0.427	9.8	0	1
2937	2000	46.0	665.0	24	1.68	0.000000	79.0	1483	25.5	39	...	78.0	43.5	547.358879	12222251.0	11.0	11.2	0.434	9.8	0	1

	Year	Adult Mortality	infant deaths	Alcohol	percentage expenditure	Hepatitis B	Measles	BMI	under-five deaths	Polio	...	Diphtheria	HIV/AIDS	GDP	Population	thinness 1-19 years	thinness 5-9 years	Income composition of resources	Schooling	Status_Developed	Status_Developing
0	2015	263.0	62	0.01	71.279624	65.0	1154	19.1	83	6.0	...	65.0	0.1	584.259210	33736494.0	17.2	17.3	0.479	10.1	0	1
1	2014	271.0	64	0.01	73.523582	62.0	492	18.6	86	58.0	...	62.0	0.1	612.696514	327582.0	17.5	17.5	0.476	10.0	0	1
2	2013	268.0	66	0.01	73.219243	64.0	430	18.1	89	62.0	...	64.0	0.1	631.744976	31731688.0	17.7	17.7	0.470	9.9	0	1
3	2012	272.0	69	0.01	78.184215	67.0	2787	17.6	93	67.0	...	67.0	0.1	669.959000	3696958.0	17.9	18.0	0.463	9.8	0	1
4	2011	275.0	71	0.01	7.097109	68.0	3013	17.2	97	68.0	...	68.0	0.1	63.537231	2978599.0	18.2	18.2	0.454	9.5	0	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2933	2004	723.0	27	4.36	0.000000	68.0	31	27.1	42	67.0	...	65.0	33.6	454.366654	12777511.0	9.4	9.4	0.407	9.2	0	1
2934	2003	715.0	26	4.06	0.000000	7.0	998	26.7	41	7.0	...	68.0	36.7	453.351155	12633897.0	9.8	9.9	0.418	9.5	0	1
2935	2002	73.0	25	4.43	0.000000	73.0	304	26.3	40	73.0	...	71.0	39.8	57.348340	125525.0	1.2	1.3	0.427	10.0	0	1
2936	2001	686.0	25	1.72	0.000000	76.0	529	25.9	39	76.0	...	75.0	42.1	548.587312	12366165.0	1.6	1.7	0.427	9.8	0	1
2937	2000	665.0	24	1.68	0.000000	79.0	1483	25.5	39	78.0	...	78.0	43.5	547.358879	12222251.0	11.0	11.2	0.434	9.8	0	1